Human and Artificial Intelligence in Healthcare
The combination of breakthroughs in AI and Machine Learning and increasing amounts of digitized medical data have generated significant excitement about the potential to automate medical decision making processes. Among these, there are significant opportunities in designing solutions for settings where AI and ML systems can work seamlessly with human experts to provide more efficient and accurate patient care. In this talk, I outline one such problem, that of medical expert disagreement. We study the application of machine learning to predict patient cases which are likely to give rise to maximal expert disagreement. We show one can develop and train AI models to predict an uncertainty score for a patient, identifying cases where large disagreements ensue, and flagging that patient for a medical second opinion. Methodologically, we formalize the importance of doing direct prediction of these uncertainty scores, instead of a two step process of diagnosis and postprocessing, evaluating on a gold-standard adjudicated dataset.
Maithra Raghu is a PhD Candidate in Computer Science at Cornell University, and a Research Scientist at Google Brain. Her research interests are in developing principled tools to empirically study the representational properties of deep neural networks, and apply these insights to deep learning applications in healthcare. She was named one of the Forbes 30 Under 30 in Science and a Rising Star in EECS.